SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3300 of 3874 papers

TitleStatusHype
Single Image Super-Resolution via CNN Architectures and TV-TV MinimizationCode1
Hybrid Residual Attention Network for Single Image Super ResolutionCode0
Super-resolution meets machine learning: approximation of measures—0
Enhanced generative adversarial network for 3D brain MRI super-resolution—0
Gated Multiple Feedback Network for Image Super-ResolutionCode0
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Improving the resolution of microscope by deconvolution after dense scan—0
Multi-level Wavelet Convolutional Neural NetworksCode0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Deep learning in ultrasound imaging—0
Distilling with Residual Network for Single Image Super Resolution—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix EstimationCode0
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models—0
CNN-based synthesis of realistic high-resolution LiDAR data—0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data—0
Resolution-invariant Person Re-IdentificationCode0
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learningCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy—0
Exemplar Guided Face Image Super-Resolution without Facial LandmarksCode0
Hierarchical Back Projection Network for Image Super-ResolutionCode0
On training deep networks for satellite image super-resolution—0
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse ProblemsCode0
Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis—0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Image-Adaptive GAN based ReconstructionCode0
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
Suppressing Model Overfitting for Image Super-Resolution Networks—0
Hybrid Function Sparse Representation towards Image Super ResolutionCode0
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network—0
Convolutional Bipartite Attractor Networks—0
A Multi-Pass GAN for Fluid Flow Super-ResolutionCode0
3D Appearance Super-Resolution with Deep LearningCode0
Super-resolution of Time-series Labels for Bootstrapped Event Detection—0
Residual Networks for Light Field Image Super-Resolution—0
Hyperspectral Image Super-Resolution With Optimized RGB GuidanceCode0
Second-Order Attention Network for Single Image Super-ResolutionCode0
Zoom to Learn, Learn to Zoom—0
ODE-Inspired Network Design for Single Image Super-Resolution—0
Towards Real Scene Super-Resolution with Raw ImagesCode1
Generative Imaging and Image Processing via Generative Encoder—0
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified